Python: [BREAKING] updated structure and samples (#875)

* updated structure and samples

* updated names and removed cross tests

* updated projects etc

* updated tests

* updated test

* test fixes

* removed devui for now

* updated all-tests task

* removed old style configs

* remove coverage from tests

* updated to unit tests with all-tests

* updated foundry everywhere

* fix azure ai tests

* fix merge tests

* fix mypy

python/samples/getting_started/workflow/control-flow/edge_condition.py

@@ -16,7 +16,7 @@ from agent_framework import (  # Core chat primitives used to build requests
     WorkflowContext,  # Per-run context and event bus
     executor,  # Decorator to declare a Python function as a workflow executor
 )
-from agent_framework.azure import AzureChatClient  # Thin client wrapper for Azure OpenAI chat models
+from agent_framework.azure import AzureOpenAIChatClient  # Thin client wrapper for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
 from pydantic import BaseModel  # Structured outputs for safer parsing
 
@@ -35,7 +35,7 @@ Purpose:
 Prerequisites:
 - You understand the basics of WorkflowBuilder, executors, and events in this framework.
 - You know the concept of edge conditions and how they gate routes using a predicate function.
-- Azure OpenAI access is configured for AzureChatClient. You should be logged in with Azure CLI (AzureCliCredential)
+- Azure OpenAI access is configured for AzureOpenAIChatClient. You should be logged in with Azure CLI (AzureCliCredential)
 and have the Azure OpenAI environment variables set as documented in the getting started chat client README.
 - The sample email resource file exists at workflow/resources/email.txt.
 
@@ -132,7 +132,7 @@ async def to_email_assistant_request(
 async def main() -> None:
     # Create agents
     # AzureCliCredential uses your current az login. This avoids embedding secrets in code.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
 
     # Agent 1. Classifies spam and returns a DetectionResult object.
     # response_format enforces that the LLM returns parsable JSON for the Pydantic model.

python/samples/getting_started/workflow/control-flow/multi_selection_edge_group.py

@@ -22,7 +22,7 @@ from agent_framework import (
     WorkflowOutputEvent,
     executor,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel
 
@@ -184,7 +184,7 @@ async def database_access(analysis: AnalysisResult, ctx: WorkflowContext[Never,
 
 async def main() -> None:
     # Agents
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
 
     email_analysis_agent = AgentExecutor(
         chat_client.create_agent(

python/samples/getting_started/workflow/control-flow/simple_loop.py

@@ -16,7 +16,7 @@ from agent_framework import (
     WorkflowOutputEvent,
     handler,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 
 """
@@ -28,7 +28,7 @@ What it does:
 - The workflow completes when the correct number is guessed.
 
 Prerequisites:
-- Azure AI/ Azure OpenAI for `AzureChatClient` agent.
+- Azure AI/ Azure OpenAI for `AzureOpenAIChatClient` agent.
 - Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`).
 """
 
@@ -122,7 +122,7 @@ async def main():
     guess_number_executor = GuessNumberExecutor((1, 100))
 
     # Agent judge setup
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
     judge_agent = AgentExecutor(
         chat_client.create_agent(
             instructions=(

python/samples/getting_started/workflow/control-flow/switch_case_edge_group.py

@@ -20,7 +20,7 @@ from agent_framework import (  # Core chat primitives used to form LLM requests
     WorkflowContext,  # Per-run context and event bus
     executor,  # Decorator to turn a function into a workflow executor
 )
-from agent_framework.azure import AzureChatClient  # Thin client for Azure OpenAI chat models
+from agent_framework.azure import AzureOpenAIChatClient  # Thin client for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
 from pydantic import BaseModel  # Structured outputs with validation
 
@@ -42,7 +42,7 @@ on that type.
 Prerequisites:
 - Familiarity with WorkflowBuilder, executors, edges, and events.
 - Understanding of switch-case edge groups and how Case and Default are evaluated in order.
-- Working Azure OpenAI configuration for AzureChatClient, with Azure CLI login and required environment variables.
+- Working Azure OpenAI configuration for AzureOpenAIChatClient, with Azure CLI login and required environment variables.
 - Access to workflow/resources/ambiguous_email.txt, or accept the inline fallback string.
 """
 
@@ -155,7 +155,7 @@ async def handle_uncertain(detection: DetectionResult, ctx: WorkflowContext[Neve
 
 async def main():
     """Main function to run the workflow."""
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
 
     # Agents. response_format enforces that the LLM returns JSON that Pydantic can validate.
     spam_detection_agent = AgentExecutor(